k-means均值聚类算法matlab代码

Sure, here is an example of the k-means clustering algorithm implemented in MATLAB:

function [centroids, assignments] = kMeansClustering(data, k)
    % Initialize centroids randomly
    nSamples = size(data, 1);
    centroidIndices = randperm(nSamples, k);
    centroids = data(centroidIndices, :);
    
    % Initialize assignments
    assignments = zeros(nSamples, 1);
    
    % Main loop
    isConverged = false;
    while ~isConverged
        % Assign data points to nearest centroid
        oldAssignments = assignments;
        distances = pdist2(data, centroids);
        [~, assignments] = min(distances, [], 2);
        
        % Update centroids
        for i = 1:k
            clusterData = data(assignments == i, :);
            if ~isempty(clusterData)
                centroids(i, :) = mean(clusterData);
            end
        end
        
        % Check convergence
        isConverged = isequal(oldAssignments, assignments);
    end
end

In this code, the `kMeansClustering` function takes two input arguments: `data` (the dataset to be clustered) and `k` (the number of clusters). It returns the final centroids (`centroids`) and the assignments of data points to clusters (`assignments`).

The algorithm starts by randomly initializing the centroids. Then, it iteratively assigns each data point to the nearest centroid and updates the centroids based on the assigned data points' mean. The iterations continue until the assignments no longer change.

You can use this function by passing your dataset (`data`) and the desired number of clusters (`k`). The function will return the final centroids and cluster assignments.

Please note that this implementation assumes you have already loaded your data into a matrix called `data`, where each row represents a data point and each column represents a feature. You may need to modify the code if your data is represented differently or if you have additional requirements.